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import gradio as gr
import pickle
import pandas as pd
# Load trained XGBoost model
with open("xgboost_trip_delay_model.pkl", "rb") as f:
model = pickle.load(f)
# List of available routes
routes = [
"254U", "13U", "16D", "116BRU", "716U", "403D", "17AEU", "18U", "14U", "116BRD", "706D", "14D", "213D", "207ED", "716D", "706U", "15CU", "403U", "118D", "16U", "137JU", "402U", "20D", "11U", "103SD", "658U", "12U", "02U", "205GU", "103VD", "137JD", "305U", "117U", "108RD", "105D", "136U", "112D", "305D", "108RU", "12D", "22D", "20U", "19U", "204D", "136D", "658D", "112U", "504D", "17AD", "21D", "504U", "107JU", "106RU", "216KD", "11D", "117D", "104U", "22U", "104D", "506U", "106RD", "21U", "105U", "17AU", "216BD", "116RD", "207EU", "116RU", "402D", "18D", "118U", "903U", "01U", "206D", "206U", "903D"
]
# Define the prediction function for Gradio
def predict_bus_delay(route_id, trip_direction, speed, trip_delay_y, hour, minute, last_stop_arrival_seconds):
data = pd.DataFrame({
"route_id": [route_id],
"trip_direction": [1 if trip_direction == "UP" else 0],
"speed": [float(speed)],
"trip_delay_y": [float(trip_delay_y)],
"hour": [int(hour)],
"minute": [int(minute)],
"last_stop_arrival_seconds": [int(last_stop_arrival_seconds)]
})
data["route_id"] = data["route_id"].astype("category")
# Make prediction
prediction = model.predict(data)[0]
return f"Predicted Bus Delay Between Stops: {prediction:.2f} seconds"
# Create Gradio interface
interface = gr.Interface(
fn=predict_bus_delay,
inputs=[
gr.Dropdown(label="Route ID", choices=routes),
gr.Dropdown(label="Direction", choices=["UP", "DN"]),
gr.Number(label="Speed"),
gr.Number(label="Previous Stop Delay"),
gr.Number(label="Hour"),
gr.Number(label="Minute"),
gr.Number(label="Last Stop Arrival (s)")
],
outputs="text"
)
# Launch the Gradio interface
interface.launch()
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